1. Field of the Invention
This invention relates generally to the manufacture of high-performance semiconductor integrated circuits. More specifically, this invention relates to a system to associate measured data parameters for specific manufacturing equipment to a particular wafer lot. Even more specifically, this invention relates to a system to associate measured data parameters for specific manufacturing equipment to a particular wafer lot using an interpolation tool or an extrapolation tool.
2. Discussion of the Related Art
In the typical semiconductor manufacturing facility, many simulation and analysis tools have been implemented to assist the process integration and device development efforts. These simulation and analysis tools, however, are typically employed to provide an indication of general trends. The latent potential of reducing the number of silicon runs and speeding up the process optimization cycle has not been fully achieved. One of the primary reasons the process optimization cycle has not been achieved is that the accuracy of the data obtained cannot be established to the degree necessary to determine the dependability of the simulations systems. The accuracy of the data obtained can only be achieved by a complete and detailed engineering calibration of the simulation system. This calibration, however, demands extensive engineering resources and data from multiple silicon production runs which, in turn, is usually only available at the latter stages of the process development or early production cycles.
In addition, process optimization for a technology that has completed qualification and is ramping-up production could receive great benefit from the extensive embedded device physics contained in advanced complex simulation tools. Despite this extensive knowledge base, statistical data analyzing tools dominate, to the near exclusion of device simulation tools, as the tools employed in the decision making process in modem semiconductor manufacturing facilities.
The main reasons for this are as follows:
1. The manufacturing data is fundamentally statistical. It is usually impossible to control, much less measure exact values for many process parameters. Moreover, if the simulation, or even the actual silicon itself, yields only a single data point without accompanying distribution information, that result is usually insufficient to justify any qualified decision. PA1 2. Process monitoring and optimization is an ongoing and reiterative sequence of fine-tuning that is dependent upon barely measurable differences which are affected by statistical fluctuation in process and complicated interactions between various process parameters. Therefore, a truly useful tool that an engineer can trust must provide a high order of data accuracy. PA1 3. Vast amounts of process variables, in-line measurements and electrical data are continually collected in the manufacturing facility (fab). Current existing simulation tools, however, cannot effectively utilize this data.
Problematically, statistical analysis alone, without integration of the existing knowledge of device physics and simulation skills, is neither flexible nor powerful enough to handle engineering changes in the process without sufficient accurate actual data from the silicon itself.
The typical semiconductor fabrication plant has a vast array of process tools that interact with silicon wafers in quantifiable ways. These interactions can be associated with either the tool, which is acting on the wafer, or with the wafer itself, which has been acted upon. For example, a metal deposition tool might have a film thickness deposited, film resistance, granularity, clarity, step coverage, hillock density etc., which could be related to the wafer. The metal deposition tool also will have a deposition rate, deposition power, film uniformity, sputter etch rate, deposition temperature, target utilization, time since last PM (preventative maintenance), last particle count data, etc., which could be associated with the tool. These types of associated can be thought of as either Entity Based, which are parameters that are descriptive of, or are of special interest to, the tool, or they may be thought of as Lot Based, which are parameters that quantify the change to the wafer. All of this data, either lot based or entity based can be stored in a large associative database. Additionally, whether the data be lot based or entity based, information of importance to the tool is associated exclusively with the lot, or vice versa, making the accessing of that additional data difficult from the other viewpoint.
A major problem is the difficulty in relating the entity-based data to a particular wafer lot. For example, a lot is processed through a certain metal deposition tool. Currently, all of the film qualities as deposited on the wafer can be easily accessed since they are lot-based. However, it is difficult to determine what the tool-based post PM particle count that was performed three days before. This is because there is no current associative link between entity-based data and the lot such that a value can be assigned. Additionally, if a link were established, there is no current method to determine the value to be assigned to the data. For example, a post PM particle count on Monday has a count of 200 and the count the following Monday is 450. If a lot is processed on Friday, there is no way to determine what value is to be used.
Therefore, what is needed is a method that can relate entity-based data to a particular lot relating to what was done to the equipment around the time when the lot was processed or that can relate lot-based data to a particular tool relating to what wafer level parameters were measured on wafer lots passing through the tool.